Merge pull request #28 from HeidiSteen/heidist-master

Added comments to the tutorial notebook

Author: HeidiSteen

Tutorial-AI-Enrichment/PythonTutorial-AzureSearch-AIEnrichment.ipynb

@@ -11,6 +11,13 @@
     "from pprint import pprint"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Name the objects created in this notebook."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -28,7 +35,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Add the name and key of your search service."
+    "Set up a search service connection."
    ]
   },
   {
@@ -50,7 +57,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Add the full connection string to your storage account. This step assumes \"basic-demo-data-pr\" as the container name. Replace that string as well if your container name is different."
+    "Create a data source connection to the external data in Blob storage. Provide a connection string to your service and the name of the container storing the sample files."
    ]
   },
   {
@@ -69,13 +76,20 @@
     "    \"connectionString\": datasourceConnectionString\n",
     "   },\n",
     "    \"container\": {\n",
-    "     \"name\": \"basic-demo-data-pr\"\n",
+    "     \"name\": \"<YOUR-CONTAINER-NAME\"\n",
     "   }\n",
     "}\n",
     "r = requests.put( endpoint + \"/datasources/\" + datasource_name, data=json.dumps(datasource_payload), headers=headers, params=params )\n",
     "print(r.status_code)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Invoke natural language processing on blob content: recognize entities, detected language, break large text into segments, detect key phrases in each segment."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -164,6 +178,13 @@
     "print(r.status_code)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define a search index to store the output."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -224,7 +245,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The next step, Create an indexer, is where all the deep processing occurs. This step takes several minutes to complete. "
+    "Create and run an indexer. This step is where deep processing occur and it takes several minutes to complete. "
    ]
   },
   {
@@ -282,6 +303,13 @@
     "print(r.status_code)\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Monitor indexer status to see if it's running."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -293,6 +321,13 @@
     "pprint(json.dumps(r.json(), indent=1))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the index defintion from the search service. This confirms the index is created."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -304,6 +339,13 @@
     "print(json.dumps(r.json(), indent=1))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Query the index to return data. This query includes a search string that selects just one field (organizations)."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,